In this paper we measure technological relatedness between industries using a dataset on product portfolios of plants. For this purpose we first develop a general methodology to extract data on co-occurrences of classes (e.g. industries) in a single entity (e.g. a plant) to construct estimates of the relatedness between the classes. The core assumption, in line with the concept of economies of scope, is that if two products are produced in the same plant, this is an indication of relatedness between the industries the two products are a part of. Unlike earlier methods, we arrive at a Revealed Relatedness (RR) index that can be interpreted on a ratio scale, allows for the use of indirect (i.e. not directly observed) information on industry relatedness, and conceptualizes relatedness as being asymmetric or directed. Direction of relatedness provides information on, for example, the most likely direction of spillovers between two classes. We also graph the RR matrices using methods borrowed from social network analysis. The result is a visualization of the “industry space” and how that changes over time with structural transformation of the economy. In order to test the validity of the framework, the industry space is used to plot structural transformation paths of regions. It is shown that the RR matrix indeed has significant explanatory power for the composition and change of a regions portfolio of manufacturing industries, in spite of the fact that regional information played no role in its derivation. This confirms the quality of our RR estimates.

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